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October 11th, 2014 - 03:03 AM
The inherent problem with algorithms whether they are based on neural networks, genetic algorithms, knowledge discovery systems or any other type of pattern recognition software is the paradox in automated pattern discovery systems in extracting random data sets and the association of interestingness as a measure of the usefulness of the data patterns revealed, because there is no prior knowledge of the likely interesting data associations to be found before automation. “If you do not expect it, you will not find the unexpected, for it is hard to find and difficult.” (Padmanabhan & Tuzhilin, 1999)
So, you see the dilemma for artificial intelligence is the probability, that a rule exists, that maps one association to another with the subjective attributes of unexpectedness is present in the fact that the unexpected is expected in the probability of chance relationships existing in any given time frame.
Many algorithms exist in determining patterns of interestingness, these then can be used as a measure of the expected quality of the data retrieval, the main problem with this approach would seem the probability of randomness in any given set of data. The random nature of data aggregation assumes that while some methods would filter interesting patterns, it would also be conceivable that many more one dimensional patterns would not be recovered without techniques that would isolate odd patterns.
Clearly, only understandable patterns can qualify as new knowledge, hence, the importance of interestingness measures in finding and tuning search heuristics in this quest for artificial intelligence.
By Anthony Fox Msc BSc Hons